Causal Inference For Biomedical Informatics

Catalog description

This course covers basic concepts and selected state-of-the-art statistical methods and theory of causal inference for biomedical informatics. It will empower students to draw causal conclusions and make predictions by mining data from observational and experimental studies. Topics include: targeted machine learning, structural equation modeling, Mendelian randomization, and heteroskedastic genomic prediction. Elementary knowledge of the use of statistical computing packages is needed.